DEA models for minimizing weight disparity in cross-efficiency evaluation

Abstract

Cross-efficiency evaluation is a commonly used approach for ranking decision-making units (DMUs) in data envelopment analysis (DEA). The weights used in the cross-efficiency evaluation may sometimes differ significantly among the inputs and outputs. This paper proposes some alternative DEA models to minimize the virtual disparity in the cross-efficiency evaluation. The proposed DEA models determine the input and output weights of each DMU in a neutral way without being aggressive or benevolent to the other DMUs. Numerical examples are tested to show the validity and effectiveness of the proposed DEA models and illustrate their significant role in reducing the number of zero weights.

Keywords:

• data envelopment analysis
• cross-efficiency evaluation
• virtual disparity
• DEA ranking

Acknowledgements

The work described in this paper was supported by the National Natural Science Foundation of China (NSFC) under the Grant Nos.70771027 and 70925004 and also partially supported by a grant from City University of Hong Kong (project no. 7002504). We would like to thank two anonymous reviewers for their constructive comments and suggestions that helped improve the paper.